Abstract: This research presents CosmoScan, a computer vision-based system designed to identify and classify galaxies into their respective morphological types using real telescope images. The model leverages Convolutional Neural Networks (CNNs) alongside traditional image processing techniques such as HOG and ORB filters to extract visual features from galaxy images. By training on the Galaxy10 dataset, CosmoScan achieves approximately 91% classification accuracy, demonstrating its efficiency in automating the galaxy morphology classification process. The project bridges the gap between classical computer vision and modern deep learning, offering a scalable solution for astronomical image analysis and research.

Index Terms: Computer Vision, Deep Learning, Galaxy Classification, CNN, Astronomy, Morphology.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131104